Mamba6mA: a Mamba-based DNA N6-methyladenine site prediction model

TL;DR

Mamba6mA is a new deep learning model that accurately predicts DNA 6mA sites across multiple species, improving on existing methods.

Contribution

Introduces Mamba6mA, a novel model using position-specific linear layers and multi-scale feature extraction for 6mA site prediction.

Findings

Mamba6mA outperforms existing models with the best MCC on 9 out of 11 species datasets.

Position-specific linear layers and multi-scale fusion module improve performance by 2.36% and 2.31%, respectively.

The model captures sequence patterns around 6mA sites, aiding in understanding epigenetic mechanisms.

Abstract

N6-methyladenine (6 mA) is an important epigenetic modification of DNA that regulates biological processes such as gene expression, transcription, replication, DNA repair, and cell cycle without altering the DNA sequence. It also plays a key role in many diseases including cancer and autoimmune diseases. Although experimental approaches such as SMRT sequencing and methylated DNA immunoprecipitation can identify 6 mA sites, they suffer from drawbacks including suboptimal sequencing quality, low signal-to-noise ratios, high costs, and time-consuming procedures. In recent years, deep learning approaches have demonstrated significant advantages in predicting 6 mA sites; however, their generalization ability still requires further improvement.

Inspired by the state space model Mamba, we propose a novel model for 6 mA site prediction, named Mamba6mA. In the Mamba6mA model, we design position-specific linear layers to replace traditional convolutional layers to facilitate capture specific positional information. Meanwhile, we construct a multi-scale feature extraction module and integrate features captured by sliding windows of different scales, feeding them into the classifier for prediction. Experimental results show that Mamba6mA achieves the best MCC on 9 out of 11 species datasets, surpassing existing state-of-the-art models. Ablation studies confirm that the position-specific linear layers and the multi-scale fusion module contribute MCC performance gains of 2.36% and 2.31%, respectively. Feature visualization analysis further reveals that the model effectively captures sequence patterns upstream and downstream of 6 mA sites providing a new technical approach for studying epigenetic modification mechanisms.

The source code for Mamba6mA is available at: https://github.com/XploreAI-Lab/Mamba6mA.

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